Operations | Monitoring | ITSM | DevOps | Cloud

Engineering teams that manage high-volume log sources, such as content delivery network (CDN) edges, streaming platforms, and authentication systems, often have to make a difficult retention tradeoff. Indexing every event keeps logs searchable during investigations, audits, and postmortems, but it can make long-term retention expensive.

At Datadog, our broad Kubernetes footprint amplifies the significance of a familiar autoscaling tradeoff: Overprovisioning wastes cloud spend, while underprovisioning threatens reliability. We built Datadog Kubernetes Autoscaling (DKA) to help teams rightsize their workloads by generating intelligent resource recommendations and automating multidimensional workload scaling. Across Datadog, adopting DKA has eliminated more than $3 million in annualized idle compute costs while reducing reliability risks.

Feature flag migrations have a reputation problem. Ask anybody who’s been through one before and you’ll hear the stories, usually from someone still a little frustrated about a bad cutover, with a postmortem or two to show for it. The reputation is mostly undeserved. While the risks are real, they’re well understood and easily controlled. Getting a migration right doesn’t require a big coordinated effort.